In the Flow, recipes are used to create new datasets by performing transformations on existing datasets. The main way to perform transformations is to use the DSS “visual recipes”, which cover a variety of common analytic use cases, like aggregations or joins.
By using visual recipes, you don’t need to write any code to perform the standard analytic transformations.
Visual recipes are not the only way to perform transformations in the Flow. You can also use code recipes, for example with SQL or HiveQL queries, or with Python or R. These code recipes offer you complete freedom for analytic cases which are not covered by DSS visual recipes.
- Prepare: Cleanse, Normalize, and Enrich
- Sync: copying datasets
- Grouping: aggregating data
- Window: analytics functions
- Distinct: get unique rows
- Join: joining datasets
- Fuzzy join: joining two datasets
- Geo join: joining datasets based on geospatial features
- Building a Geo join recipe in the flow
- Detailed recipe configuration steps
- Splitting datasets
- Top N: retrieve first N rows
- Stacking datasets
- Sampling datasets
- Sort: order values
- Pivot recipe
- Push to editable recipe
- Download recipe
- List Folder Contents